Diagnostic Reliability of Headset-Type Continuous Video EEG Monitoring for Detection of ICU Patterns and NCSE in Patients with Altered Mental Status with Unknown Etiology
Diagnostic Reliability of Headset-Type Continuous Video EEG Monitoring for Detection of ICU Patterns and NCSE in Patients with Altered Mental Status with Unknown Etiology
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DOI：
10.1007/s12028-019-00863-9
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发表时间：
2019-10-15
影响因子：
3.5
通讯作者：
Kubota, Yuichi
中科院分区：
文献类型：
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作者：
Egawa, Satoshi;Hifumi, Toru;Kubota, Yuichi
背景/目的 简化的连续脑电图 (cEEG) 监测在检测癫痫发作方面已显示出改进；然而，它不足以检测异常脑电图模式，例如周期性放电（PD）、节律性δ活动（RDA）、棘波和波（SW）、连续慢波（CS）以及非惊厥性癫痫持续状态（NCSE）。耳机式连续视频脑电图监测（HS-cv 脑电图监测；AE-120A EEG 耳机 (TM)，日本东京 Nihon Kohden）是最近开发的一种易于使用的八通道技术。然而，其利用原始脑电图数据检测异常脑电图模式的能力尚未得到全面评估。我们的目的是检查 HS-cv 脑电图监测在检测病因不明的精神状态改变 (AMS) 患者的异常脑电图模式和 NCSE 方面的诊断准确性。我们还评估了在这些患者中启动 HS-cv EEG 监测所需的时间。方法 我们对 2017 年 1 月至 12 月期间在日本埼玉县浅台中央综合医院神经重症监护病房因 AMS 入院的患者进行前瞻性观察和回顾性检查。我们排除了因记录困难等各种原因导致数据缺失的患者，以及在 HS-cv EEG 和传统 cEEG (C-cEEG) 监测之间意识已恢复的患者。对于纳入的患者，我们进行了 HS-cv EEG 监测，然后进行了 C-cEEG 监测。通过国际 10-20 系统的 C-cEEG 监测确诊。作为主要结果，我们验证了 HS-cv EEG 监测在检测异常 EEG 模式（包括 PD、RDA、SW 和 CS）、检测 PD 的存在以及检测 NCSE 方面的敏感性和特异性。作为次要结果，我们计算了做出决定后启动 HS-cv 脑电图监测的时间。结果 50 例患者（76.9%）进入最终分析。中位年龄为 72 岁，66% 的患者为男性。 HS-cv EEG监测检测异常EEG模式的敏感性和特异性分别为0.974（0.865-0.999）和0.909（0.587-0.998），检测PD的敏感性和特异性分别为0.824（0.566-0.926）和0.970（0.842-0.999）。我们使用 HS-cv EEG 监测诊断了 13 名 (26%) NCSE 患者，检测 NCSE 的敏感性和特异性分别为 0.706 (0.440-0.897) 和 0.970 (0.842-0.999)。启动 HS-cv EEG 所需的中位时间为 57 分钟 (5-142)。结论 HS-cv EEG 监测在检测异常 EEG 模式方面高度可靠，对于 PD 和 NCSE 具有中等可靠性，并且可以在病因不明的 AMS 患者中快速启动 cEEG 监测。
Background/Objective Simplified continuous electroencephalogram (cEEG) monitoring has shown improvement in detecting seizures; however, it is insufficient in detecting abnormal EEG patterns, such as periodic discharges (PDs), rhythmic delta activity (RDA), spikes and waves (SW), and continuous slow wave (CS), as well as nonconvulsive status epilepticus (NCSE). Headset-type continuous video EEG monitoring (HS-cv EEG monitoring; AE-120A EEG Headset (TM), Nihon Kohden, Tokyo, Japan) is a recently developed easy-to-use technology with eight channels. However, its ability to detect abnormal EEG patterns with raw EEG data has not been comprehensively evaluated. We aimed to examine the diagnostic accuracy of HS-cv EEG monitoring in detecting abnormal EEG patterns and NCSE in patients with altered mental status (AMS) with unknown etiology. We also evaluated the time required to initiate HS-cv EEG monitoring in these patients. Methods We prospectively observed and retrospectively examined patients who were admitted with AMS between January and December 2017 at the neurointensive care unit at Asakadai Central General Hospital, Saitama, Japan. We excluded patients whose data were missing for various reasons, such as difficulties in recording, and those whose consciousness had recovered between HS-cv EEG and conventional cEEG (C-cEEG) monitoring. For the included patients, we performed HS-cv EEG monitoring followed by C-cEEG monitoring. Definitive diagnosis was confirmed by C-cEEG monitoring with the international 10-20 system. As the primary outcome, we verified the sensitivity and specificity of HS-cv EEG monitoring in detecting abnormal EEG patterns including PDs, RDA, SW, and CS, in detecting the presence of PDs, and in detecting NCSE. As the secondary outcome, we calculated the time to initiate HS-cv EEG monitoring after making the decision. Results Fifty patients (76.9%) were included in the final analyses. The median age was 72 years, and 66% of the patients were male. The sensitivity and specificity of HS-cv EEG monitoring for detecting abnormal EEG patterns were 0.974 (0.865-0.999) and 0.909 (0.587-0.998), respectively, and for detecting PDs were 0.824 (0.566-0.926) and 0.970 (0.842-0.999), respectively. We diagnosed 13 (26%) patients with NCSE using HS-cv EEG monitoring and could detect NCSE with a sensitivity and specificity of 0.706 (0.440-0.897) and 0.970 (0.842-0.999), respectively. The median time needed to initiate HS-cv EEG was 57 min (5-142). Conclusions HS-cv EEG monitoring is highly reliable in detecting abnormal EEG patterns, with moderate reliability for PDs and NCSE, and rapidly initiates cEEG monitoring in patients with AMS with unknown etiology.